notebooks/svglib.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.chdir(\"..\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from deepsvg.svglib.geom import Point, Angle\n",
    "from deepsvg.svglib.svg import SVG\n",
    "from deepsvg.svglib.utils import make_grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading & displaying"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dolphin = SVG.load_svg(\"docs/imgs/dolphin.svg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dolphin.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For proper displaying, SVGlib expects SVGs to be drawn in a normalized viewbox of size 24x24. Stroke-width will appear to thin if a larger viewbox is used and too thick if the viewbox is too small."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bbox(0.0 0.0 294.8680114746094 294.8680114746094)\n"
     ]
    }
   ],
   "source": [
    "print(dolphin.viewbox)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can **normalize** an SVG with the `normalize` method which will properly scale the points to fit a 24x24 window."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bbox(0.0 0.0 24.0 24.0)\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dolphin.normalize()\n",
    "print(dolphin.viewbox)\n",
    "dolphin.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `with_points` option lets you display the individual points of the path:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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   ],
   "source": [
    "dolphin.draw(with_points=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us \"unzoom\" a little, so that there is a little more free room on the borders of the image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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     "metadata": {},
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   ],
   "source": [
    "dolphin.zoom(0.9).draw(with_points=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For easier training of DeepSVG, the following \"canonicalization\" strategy is used:\n",
    "- an SVG path is oriented clockwise and starts from the topmost-leftmost point.\n",
    "- the path is simplified, so that points lie at approximately equal distance between each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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    "Our simplification heuristic uses a combination of Ramer-Douglas-Peucker and Philip J. Schneider algorithms."
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    "Finally, generating a GIF from the drawing is as easy as:"
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controls>Sorry, seems like your browser doesn't support HTML5 audio/video</video></div>"
      ],
      "text/plain": [
       "<moviepy.video.io.html_tools.HTML2 object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dolphin.animate(frame_duration=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distinguishing individual paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "svg = SVG.load_svg(\"docs/imgs/rainbow.svg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `draw_colored` makes it easy to distinguish the individual paths of an SVG image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
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      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "svg.draw_colored()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rotation and grid displaying"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a code sample showing how one can duplicate an existing icon, rotate it and display both icons side-by-side."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "clock = SVG.load_svg(\"docs/imgs/clock.svg\")\n",
    "clock2 = clock.copy().rotate(Angle(45))\n",
    "clock3 = clock.copy().rotate(Angle(90))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_grid([clock, clock2, clock3]).draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Manipulating individual paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
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       "<path d=\"M17.760000228881836 9.600000381469727 L17.760000228881836 9.600000381469727 C18.3551025390625 9.718419075012207 18.8338565826416 9.869729042053223 19.090347290039062 10.488951683044434 C19.47652816772461 11.421273231506348 18.76919937133789 12.479999542236328 17.760000228881836 12.479999542236328 C16.75080108642578 12.479999542236328 16.04347038269043 11.42127513885498 16.42965316772461 10.488951683044434 C16.686141967773438 9.869728088378906 17.164894104003906 9.718420028686523 17.760000228881836 9.600000381469727 L17.760000228881836 9.600000381469727\" fill=\"none\" filling=\"0\" stroke=\"black\" stroke-opacity=\"1.0\" stroke-width=\".3\"/>\n",
       "<path d=\"M22.559999465942383 12.479999542236328 L22.559999465942383 10.079999923706055 L22.559999465942383 7.680000305175781 C22.38687515258789 7.261876106262207 22.018123626708984 6.893125057220459 21.599998474121094 6.720000267028809 L18.399999618530273 6.720000267028809 L15.199999809265137 6.720000743865967 L12.0 6.720000267028809 L8.800000190734863 6.720000743865967 L5.599999904632568 6.720000267028809 L2.40000057220459 6.720000267028809 C1.9818763732910156 6.893125057220459 1.6131258010864258 7.261876106262207 1.4400005340576172 7.680000305175781 L1.4400005340576172 10.079999923706055 L1.4400005340576172 12.479999542236328\" fill=\"none\" filling=\"0\" stroke=\"black\" stroke-opacity=\"1.0\" stroke-width=\".3\"/>\n",
       "<path d=\"M13.732800483703613 16.507200241088867 L11.666399955749512 14.253600120544434 L9.600000381469727 12.0 L7.840000629425049 13.920001029968262 L6.080000400543213 15.84000015258789 L4.320000171661377 17.760000228881836\" fill=\"none\" filling=\"0\" stroke=\"black\" stroke-opacity=\"1.0\" stroke-width=\".3\"/>\n",
       "<path d=\"M19.68000030517578 18.239999771118164 L17.760000228881836 16.31999969482422 L15.84000015258789 14.399999618530273 L13.920000076293945 16.31999969482422 L12.0 18.239999771118164\" fill=\"none\" filling=\"0\" stroke=\"black\" stroke-opacity=\"1.0\" stroke-width=\".3\"/></svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "canvas = SVG.load_svg(\"docs/imgs/canvas.svg\")\n",
    "canvas.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us scale the mountain on the right and translate the sun to the left."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg height=\"200px\" viewBox=\"0.0 0.0 24.0 24.0\" width=\"200px\" xmlns=\"http://www.w3.org/2000/svg\"><path d=\"M21.600000381469727 19.200000762939453 L18.720001220703125 16.31999969482422 L15.84000015258789 13.4399995803833 L12.960000038146973 16.31999969482422 L10.079999923706055 19.200000762939453\" fill=\"none\" filling=\"0\" stroke=\"black\" stroke-opacity=\"1.0\" stroke-width=\".3\"/></svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mountain2 = canvas[4]\n",
    "center = mountain2.bbox().center\n",
    "mountain2.translate(-center).scale(1.5).translate(center).draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg height=\"200px\" viewBox=\"0.0 0.0 24.0 24.0\" width=\"200px\" xmlns=\"http://www.w3.org/2000/svg\"><path d=\"M7.760000228881836 9.600000381469727 L7.760000228881836 9.600000381469727 C8.3551025390625 9.718419075012207 8.833856582641602 9.869729042053223 9.090347290039062 10.488951683044434 C9.47652816772461 11.421273231506348 8.76919937133789 12.479999542236328 7.760000228881836 12.479999542236328 C6.750801086425781 12.479999542236328 6.04347038269043 11.42127513885498 6.429653167724609 10.488951683044434 C6.6861419677734375 9.869728088378906 7.164894104003906 9.718420028686523 7.760000228881836 9.600000381469727 L7.760000228881836 9.600000381469727\" fill=\"none\" filling=\"0\" stroke=\"black\" stroke-opacity=\"1.0\" stroke-width=\".3\"/></svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sun = canvas[1]\n",
    "sun.translate(Point(-10, 0)).draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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